1. Field of the Invention
The present invention relates to language translation and more specifically to machine translation when target words are associated with the entire source sentence without the need to compute local associations between source words and target words.
2. Introduction
The problem of machine translation can be viewed as consisting of two subproblems: (a) lexical selection, where appropriate target language lexical items are chosen for each source language lexical item and (b) lexical reordering, where the chosen target language lexical items are rearranged to produce a meaningful target language string. Most of the previous work on statistical machine translation employs a word-alignment algorithm that provides local associations between source words and target words. See, e.g., Brown, Pietra, Pietra, and Mercer, “The Mathematics of Machine Translation: Parameter Estimation,” Computational Linguistics, 16(2):263-312, 1993, incorporated herein by reference. The source-to-target word alignments are sometimes augmented with target-to-source word alignments in order to improve the precision of these local associations. Further, the word-level alignments are extended to phrase-level alignments in order to increase the extent of local associations. The phrasal associations compile some amount of (local) lexical reordering of the target words—those permitted by the size of the phrase. Most of the state-of-the-art machine translation systems use these phrase-level associations in conjunction with a target language model to produce the target sentence. There is almost no emphasis on lexical reordering other than the local reorderings permitted within the phrasal alignments. A few exceptions are the hierarchical (possibly syntax-based) transduction models and the string transduction model. Examples of hierarchical transduction models can be found in the following papers: Wu, “Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora,” Computational Linguistics, 23(3): 377-404, 1997; Alshawi, Bangalore, and Douglas, “Automatic acquisition of hierarchical transduction models for machine translation,” ACL, 1998; Yamada and Knight, “A syntax-based statistical translation model,” Proceedings of 39 ACL, 2001; and Chiang, “A hierarchical phrase-based model for statistical machine translation,” Proceedings of the ACL Conference, 2005, incorporated herein by reference. For an example of a string transduction model, see Kanthak, Vilar, Matusov, Zens, and Ney, “Novel reordering approaches in phrase-based statistical machine translation,” Proceedings of the ACL Workshop on Building and Using Parallel/Texts, 2005, incorporated herein by reference.
Focusing on information associated with local reordering in the context of language translation can ignore other sources of information which may provide a benefit in improving the product of translation. Therefore, what is needed in the art is an improved method of providing machine translations.